Microsoft Azure AI Engineer Associate AI-102 Practice Question
You are creating a project in Azure AI Foundry. The project must 1) expose an existing private Azure OpenAI gpt-35-turbo deployment, 2) hold a refreshable, managed vector index of the product catalog, and 3) let prompt-flow authors use both services without coding secrets. Which resources should you add?
Add the existing Azure OpenAI resource and create a new Vector Index resource in the project.
Create new Azure Cosmos DB for NoSQL and store the embeddings there, then reference it from prompt flow.
Add the existing Azure OpenAI resource and link an Azure Key Vault that stores the vector index connection string.
Use Azure App Configuration to store both the OpenAI endpoint and the product catalog embeddings connection.
A private Azure OpenAI deployment is surfaced to Foundry projects by adding the existing Azure OpenAI resource. To hold embeddings that can be refreshed, you create a managed Vector Index resource inside the project, which automatically generates the secure connection needed by prompt flows. A Key Vault alone, Cosmos DB, or App Configuration do not provide integrated vector-index functionality.
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What is a Vector Index in Azure AI Foundry?
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